Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. Claims 13-19 refer to claim 12 as system while claim 12 is a method claim. Appropriate action is required..
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
the claimed invention is directed to an abstract Idea without significantly more.
Claims 1, 12 and 20 recite:
“…that generates a plurality of corresponding AI generated source code…”
“… scan the plurality of corresponding AI generated source code to identify an issue; identify, …..a first input parameter from the plurality of sets of input parameters that is associated with the identified issue; and modify, ….and based on the first input parameter, a new first input parameter provided to the first AI algorithm, wherein the first new input parameter is used to generate a new corresponding AI generated source code.….”;
that are certainly a mental process that a person can carry out mentally through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper.
Claims 1, 12 20 additionally recite:
“… using a second AI algorithm,” and ” by the second AI algorithm…”
“a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to:
capture a plurality of sets input parameters, wherein the captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm…., wherein each set of the captured plurality of sets of input parameters comprises one or more input parameters. Claim 20 additionally recites: “non-transient computer readable medium” and “processor”;
The additional elements “processor/microprocessor”,” non-transient computer readable medium “computer-readable medium” and “ first/second Artificial Intelligence (AI) algorithm” are directed to generic computer components which are recited at a high level of generality, but to nothing more than an instruction implement “to apply” the abstract idea using a generic computer. See MPEP 2106.05(f).
The additional elements “…capture a plurality of sets input parameters …” are directed to storing, retrieving and manipulating data, that is mere data gathering/storing and does nothing more than adding insignificant extra solution activity to the judicial exception, that is a mere data gathering. See MPEP 2106.05(g).
Claims 1, 12, 20 additional elements do not add meaningful limits to practicing the abstract idea, but to nothing more than an instruction to apply the abstract idea using a generic computer. Thus, the additional elements fail to integrate the judicial exception into a practical application.
Claims 1, 8, 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with regard to integration of the abstract idea into a practical application The additional elements “processor/microprocessor”,” non-transient computer readable medium “computer-readable medium” and “ first/second Artificial Intelligence (AI) algorithm” are generic computer component used as a tool to perform the abstract idea.
With regard to the additional element “…capture a plurality of sets input parameters …” the court have found and identified retrieving , storing and manipulating information as well understood, routine conventional activity in the art. See MPEP 2106.05(d).
Accordingly, the additional elements do not provide an inventive concept, thus claims 1, 12, 20 are not patent eligible.
-Dependents claims 2-7, 9-14 and 16-20:
Claim 2 recites : “identified based on a ranking score and/or a type of the identified issue” that is a mental process;
Claims 3,13 and 4, 14 recite: “wherein the new first input parameter is displayed in a user interface”, and “wherein an alternate new input parameter is also displayed in the user interface” that is a data output
And also recite “for a user’s approval/disapproval based on the first new input parameter being the same as the first input parameter or an input parameter similar to the first input parameter” and “the user can select to replace the first input parameter or the input parameter similar to the identified first input parameter with the alternate new input parameter”; that is a mental process executed by the user.
Claim 5 and 15 recite: “ wherein an output issue scanner is a Generative Adversarial Network (GAN) discriminator and, and wherein the second AI algorithm is a GAN generator, and wherein the GAN discriminator and the GAN generator comprise a GAN model” that is data description and “the GAN discriminator is used to scan the plurality of corresponding AI generated source code” that is using a computer to apply the mental process and as discussed above it fails to integrate the judicial exception into a practical application nor sufficient to amount to significantly more than the judicial exception
Claim 6 and 16 recite: “wherein the identified issue has a corresponding snippet of source code, wherein the corresponding snippet of source code is a second new input parameter provided to the first AI algorithm to generate the new corresponding AI generated source code, and wherein the corresponding snippet of source code is a negative input to the first AI algorithm” that is data description and “the first AI algorithm to not generate source code similar to or the same as the corresponding snippet of source code” that is using a computer to apply the mental process and as discussed above it fails to integrate the judicial exception into a practical application nor sufficient to amount to significantly more than the judicial exception
Claim 7 and 17 recite “wherein the corresponding snippet of source code comprises a plurality of corresponding snippets of source code for a plurality of issues identified in the plurality of corresponding AI generated source code” that is a data description for a mental process (identified) output.
Claims 8, recite: “wherein the plurality of corresponding snippets of source code are displayed to a user” that is a data output.
And also recite “so the user can determine which ones of the plurality of snippets of source code can be used for the second new input parameter.” that is a mental process executed by the user.
Claim 9and 18 recite : “ wherein a snippet of the identified first issue is added to a training set of the first AI algorithm and wherein the first AI algorithm is retrained using the snippet of the identified first issue as a negative input for training the first AI algorithm” that is a mental process.
Claim 10 recite: “wherein a user can select one of the plurality of corresponding AI generated source code based on a ranking and/or a number of issues in each of the plurality of corresponding AI generated source code” that is a mental process.
Claim 11and 19 recite: “wherein the first input parameter further comprises a snippet of source code that is identified in a training set used to train the first AI algorithm and wherein the snippet of source code is used as a negative input into the first AI algorithm” that is a data description used in a mental process.
Dependents claims 2-11 and 13-19 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-8, 12-14, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al US20250117195A1 in view of Hawker et al US20240385942A1.
As per claim 1, Rieken discloses a system comprising:
a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor:
[0079] An example system (FIG. 1, 102A-102M, 106A-106N; FIG. 5, 500; FIG. 7, 700) comprises a processor system (FIG. 7, 702) and a memory (FIG. 7, 704, 708, 710) that stores computer-executable instructions. The computer-executable instructions are executable by the processor system to, based at least on code (FIG. 5, 538) being developed in a developer tool, provide (FIG. 2, 202) an interface element in a user interface (FIG. 5, 556) of the developer tool. ”;
cause the microprocessor to: capture a plurality of sets input parameters:
[0035]” In an aspect, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification by providing the code modification AI prompt 560 together with the snippet as inputs to the artificial intelligence model 516.”;
wherein the captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm that generates a plurality of corresponding AI generated source code:
[0035]“In accordance with this aspect, the snippet includes context regarding the code modification AI prompt 560. For example, the code modification AI prompt 560 may be included in the AI prompt(s) 540. In another example, the snippet may be included in the snippet(s) 542. In yet another example, the code modification AI prompt 560 may be written by a developer of the code 538. In accordance with this example, the control logic 514 may generate (e.g., automatically generate) a system-generated prompt that includes the snippet based at least on the code modification AI prompt 560. In further accordance with this example, the control logic 514 may provide the code modification AI prompt 560 together with the system-generated prompt, which includes context regarding the code modification AI prompt 560, as inputs to the artificial intelligence model 516”;
wherein each set of the captured plurality of sets of input parameters comprises one or more input parameters:
[0048]”In accordance with this embodiment, the method of flowchart 200 further includes causing the artificial intelligence model to correct the programming error by providing a second artificial intelligence prompt, which specifies that the programming error is to be corrected, together with at least a portion of the code that includes the programming error as second inputs to the artificial intelligence model. The second artificial intelligence prompt may be a system-generated prompt or a prompt that is generated by a developer of the code.
wherein the first new input parameter is used to generate a new corresponding AI generated source code.
[0035]” AI prompt 560 via the interface element, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification on at least a snippet of the code 538 to provide a modified code snippet, which is included in modified snippet(s) 562. In an aspect, the control logic 514 automatically causes the artificial intelligence model 516 to perform the modification by providing the code modification AI prompt 560 together with the snippet as inputs to the artificial intelligence model 516”;
But not explicitly:
scan the plurality of corresponding AI generated source code to identify an issue:
identify, using a second AI algorithm, a first input parameter from the plurality of sets of input parameters that is associated with the identified issue;
and modify, by the second AI algorithm and based on the first input parameter, a new first input parameter provided to the first AI algorithm, wherein the first new input parameter is used to generate a new corresponding AI generated source code.
Hawker discloses:
scan the plurality of corresponding AI generated source code to identify an issue:
[0045]”It is also assumed that an automated code scanner (e.g., code scanning system 116) is configured to intermittently scan code 130 in order to surface information to enhance issue resolution, as indicated by block 258 in the flow diagram of FIG. 3. It will be noted that, while code scanning system 116 is shown as a separate tool in FIG. 1, system 116 could be deployed within the continuous integration system 112 and triggered when a build operation is performed, or in other ways”;
identify, using a second AI algorithm, a first input parameter from the plurality of sets of input parameters that is associated with the identified issue:
[0047]”Issue detection engine 162 then scans code 130 and/or other sources of information for issues identified by issue identifiers 134, that may have workarounds 136 associated with them. As discussed above, issue identifiers 134 can include webpages, URL, link, etc. Scanning the code and/or other sources of information for issues and workarounds is indicated by block 278 in the flow diagram of FIG. 3.
and modify, by the second AI algorithm and based on the first input parameter, a new first input parameter provided to the first AI algorithm:
[0063] “ In one example, workaround code identifier 192 identifies different code patterns or code snippets that have been suggested in the extracted data as possible workarounds that can be used to address the identified issue. For example, natural language processor 190 or AI response processor 198 can identify pieces of code in the issue-related data that appear to be possible or suggested workarounds that may be implemented to address the identified issue.”
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Hawker into teachings of Rieken to perform an operation to clean up the code in a code base given a change in status. Furthermore, to reduce a laborious and error prone process in which a developer tries to locate issues in the code base, identify the status of those issues to determine whether the status has changed. And finally, to use a code scanning system in the code base to locate issue identifiers that identify issues for which a corresponding workaround has been implemented. The code scanning system automatically identifies the status of each issue to determine whether there has been a status change. If there has been a status change, the code scanning system identifies one or more suggested operations that should be performed in response to the status change and generates an output identifying the issue, the status change, and the suggested operations, for operator interaction.[Hawker 0016].
As per claim 2, the rejection of claim 1 is incorporated and furthermore Rieken and Hawker disclose:
wherein the first input parameter is identified based on a ranking score and/or a type of the identified issue.
Rieken [0038] “ Examples of a portion of text include but are not limited to a comment, a control-flow statement, a keyword, and a variable. Marking of a warning or an error in code may include underlining a portion of the code to which the warning or error applies (e.g., using a red squiggly line). “;
[0048]” In accordance with this embodiment, the error determination logic 534 generates error information 550 to indicate (e.g., specify or describe) the programming error. “;
As per claim 3, the rejection of claim 1 is incorporated and furthermore Rieken and Hawker disclose:
wherein the new first input parameter is displayed, in a user interface, for a user’s approval/disapproval based on the first new input parameter being the same as the first input parameter or an input parameter similar to the first input parameter.
Rieken[0073]”The AI-modified code recommendation logic identifies a programming error in the modified snippet and provides an error indicator 622 in the processed version of the modified snippet 618, which indicates a symbol that is associated with the programming error. For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification”;
As per claim 4, the rejection of claim 3 is incorporated and furthermore Rieken and Hawker disclose:
wherein an alternate new input parameter is also displayed in the user interface and wherein the user can select to replace the first input parameter or the input parameter similar to the identified first input parameter with the alternate new input parameter.
Reiken [0073]”The AI-modified code recommendation logic identifies a programming error in the modified snippet and provides an error indicator 622 in the processed version of the modified snippet 618, which indicates a symbol that is associated with the programming error. For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification”;
As per claim 6, the rejection of claim 1 is incorporated and furthermore Roieken and Hawker disclose:
wherein the identified issue has a corresponding snippet of source code:
Reiken [0048]”In an example error fixing embodiment, the method of flowchart 200 further includes determining that replacement of the snippet in the code with the modified snippet causes the code to include a programming error. “
wherein the corresponding snippet of source code is a second new input parameter provided to the first AI algorithm to generate the new corresponding AI generated source code:
Reiken [0048]“In an example implementation, the error determination logic 534 determines that replacement of the snippet in the code 538 with the modified snippet causes the code 538 to include the programming error. In accordance with this embodiment, the error determination logic 534 generates error information 550 to indicate (e.g., specify or describe) the programming error.”;
and wherein the corresponding snippet of source code is a negative input to the first AI algorithm that causes the first AI algorithm to not generate source code similar to or the same as he corresponding snippet of source code.
Reiken [0048]“In accordance with this embodiment, the method of flowchart 200 further includes causing the artificial intelligence model to correct the programming error by providing a second artificial intelligence prompt, which specifies that the programming error is to be corrected, together with at least a portion of the code that includes the programming error as second inputs to the artificial intelligence model.”
As per claim 7 the rejection of claim 6 is incorporated and furthermore Rieken and Hawker disclose:
wherein the corresponding snippet of source code comprises a plurality of corresponding snippets of source code for a plurality of issues identified in the plurality of corresponding AI generated source code.
Reiken [0073] “For instance, the AI-modified code recommendation logic recognizes that the symbol “_data” has been changed to “data”, and this has resulted in the programming error. By identifying the programming error in the processed version of the modified snippet 618, the AI-modified code recommendation logic enables the developer of the code 602 to see (and potentially correct) the programming error prior to acceptance of the modification. It should be noted that debugging functionality of the developer tool, which is available to fix errors in the code 600, is also available to fix errors in the modified snippet.”;
As per claim 8 the rejection of claim 7 is incorporated and furthermore Rieken and Hawker disclose:
wherein the plurality of corresponding snippets of source code are displayed to a user so the user can determine which ones of the plurality of snippets of source code can be used for the second new input parameter:
Rieken [0065] “At step 406, a determination is made that the modification of the snippet is accepted via the second interface element. In an example implementation, the replacement postponing logic 530 determines that the modification of the snippet is accepted via the second interface element. For example, the replacement postponing logic 530 may make the determination based on receipt of a modification acceptance indicator 570, which indicates that the modification of the snippet is accepted via the second interface element
Claims 12, 13, 14, 16, 17 are the method claims corresponding to system claims 1, 3, 4, 6, 7 and rejected under the same rational set forth in connection with the rejection of claims 1, 3, 4, 6, 7 above.
Claim 20 is the non-transient computer readable medium to system claim 1 and rejected under the same rational set forth in connection with the rejection of claim 1 above.
Claims 5, 9-11 , 15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rieken et al US20250117195A1 in view of Hawker et al US20240385942A1 and further in view of Kohisseri et al US20220188079A1
As per claim 5, the rejection of claim 3 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein an output issue scanner is a Generative Adversarial Network (GAN) discriminator and the GAN discriminator is used to scan the plurality of corresponding AI generated source code:
and wherein the second AI algorithm is a GAN generator, and wherein the GAN discriminator and the GAN generator comprise a GAN model.
Kohisseri discloses:
wherein an output issue scanner is a Generative Adversarial Network (GAN) discriminator and the GAN discriminator is used to scan the plurality of corresponding AI generated source code:
[0098]“In an embodiment, the code creation model may use the description from technology websites and its code and/or command representations to learn using Generative adversarial networks (GAN) representations.
and wherein the second AI algorithm is a GAN generator, and wherein the GAN discriminator and the GAN generator comprise a GAN model.
[0007]” Subsequently, the instructions cause the processor to generate one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes.[Kohisseri 0006].
As per claim 9 the rejection of claim 1 is incorporated and furthermore Reiken and Hawker do not explicitly disclose:
wherein a snippet of the identified first issue is added to a training set of the first AI algorithm and wherein the first AI algorithm is retrained using the snippet of the identified first issue as a negative input for training the first AI algorithm:
Kohisseri discloses:
wherein a snippet of the identified first issue is added to a training set of the first AI algorithm and wherein the first AI algorithm is retrained using the snippet of the identified first issue as a negative input for training the first AI algorithm:
[0098]”In an embodiment, the technology learning model (or an information portal code) may use the one or more source codes 213 and the descriptions of the one or more source codes 213 to train the technology learning model and to learn each code and its representations. In an embodiment, the issue resolution model may use code snippets corresponding to various issue description for learning the code and its representations.
[0096] “ In an embodiment, the source code generator 105 may collect the user feedback indicating a complete acceptance, partial acceptance or rejection of the executable source code 109 by the user 101, and use the collected user feedback for training a recommendation model used for validating the one or more best-fit source codes.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes.[Kohisseri 0006].
As per claim 10 the rejection of claim 1 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein a user can select one of the plurality of corresponding AI generated source code based on a ranking and/or a number of issues in each of the plurality of corresponding AI generated source code:
Kohisseri discloses:
wherein a user can select one of the plurality of corresponding AI generated source code based on a ranking and/or a number of issues in each of the plurality of corresponding AI generated source code:
[0094] In an embodiment, selection of the one or more source codes 213 in the one or more clusters may be performed based on at least one of a ranking associated with each of the one or more source codes 213 or a level of match between results obtained by compiling the one or more source codes 213 and results expected by the user 101.
[0096] In an embodiment, the one or more best-fit source codes may be validated based on weightages associated with each of the one or more best-fit source codes. As an example, the weightages for each of the one or more best-fit source codes may be computed based on number of times that each of the one or more best-fit source codes are previously accepted by the user 101.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes.[Kohisseri 0006].
As per claim 11 the rejection of claim 1 is incorporated and furthermore Rieken and Hawker do not explicitly disclose:
wherein the first input parameter further comprises a snippet of source code that is identified in a training set used to train the first AI algorithm and wherein the snippet of source code is used as a negative input into the first AI algorithm:
Kohisseri discloses:
wherein the first input parameter further comprises a snippet of source code that is identified in a training set used to train the first AI algorithm and wherein the snippet of source code is used as a negative input into the first AI algorithm:
[0084] In an embodiment, after recommending the executable source code 109 to the user 101, the source code generator 105 may collect reviews and feedback from the user 101 on the presented executable source code 109. As an example, the user feedback may be at least one that the user 101 has “fully accepted”, “partially accepted” or “not accepted” the given executable source code 109. In case, the user 101 has “fully accepted” the given executable source code 109, the selected executable source code 109 may be fed back to the pre-trained code generation model 106 as an input to reinforcement learning of the pre-trained code generation model 106. Also, the pre-trained code generation model 106 may consider this instance as a “reward” and learn to recommend similar executable source codes 109 for similar application requirements.
[0085] “Similar learning strategy is followed when the user feedback is “not accepted”.”;
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Kohisseri into teachings of Rieken and Hawker for receiving user inputs related to requirements of an application from a user and identifying an application flow corresponding to the application by processing the user inputs. Further, the method comprises generating one or more source codes for the application flow using at least one pre-trained code generation model. The at least one pre-trained code generation model generates the one or more source codes based on the user inputs, and one or more reference source codes retrieved from predetermined code repositories. Upon generating the one or more source codes, the method comprises determining one or more best-fit source codes for the application based on similarities among each of the one or more source codes.[Kohisseri 0006].
Claims 15, 18, 19 are the method claims corresponding to system claims 5, 9, 11 and rejected under the same rational set forth in connection with the rejection of claims 5, 9, 11 above.
Pertinent arts:
US 20250117201 A1:
A trust score for each code module of the plurality of code modules is determined, wherein the trust score includes a first trust score component for issue identification and a second trust score component for issue remediation, and wherein the trust score is based on a source of each code module selected from a group of a human-generated source and an artificial intelligence model-generated source.
US 20200272435 A1:
generating a computer program using artificial intelligence module include generating logic programming by analyzing natural language in sample input data received from an external source.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRAHIM BOURZIK whose telephone number is (571)270-7155. The examiner can normally be reached Monday-Friday (8-4:30).
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/BRAHIM BOURZIK/ Examiner, Art Unit 2191
/WEI Y MUI/ Supervisory Patent Examiner, Art Unit 2191